U.S. patent number 11,017,275 [Application Number 16/530,766] was granted by the patent office on 2021-05-25 for method and apparatus for multi-scale sar image recognition based on attention mechanism.
This patent grant is currently assigned to WUYI University. The grantee listed for this patent is WUYI University. Invention is credited to Wenbo Deng, Junying Gan, Qirui Ke, Ying Xu, Zilu Ying, Junying Zeng, Yikui Zhai, Wenlue Zhou.
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United States Patent |
11,017,275 |
Zhai , et al. |
May 25, 2021 |
Method and apparatus for multi-scale SAR image recognition based on
attention mechanism
Abstract
Disclosed are a method and an apparatus for multi-scale SAR
image recognition based on attention mechanism. According to the
method, a whole image recognition network is adjusted by training a
SAR training image by an attention prediction subnet, a
region-of-interest positioning subnet and an image classification
subnet in combination with a network loss, which greatly improves a
network performance; and in addition, an attention prediction map
is generated by attention mechanism to position a most prominent
feature part in the SAR image, which greatly eliminates a
redundancy of image features in a machine vision, effectively
determines a region-of-interest, reduces interference of image
noises, greatly reduces an image processing time, improves a target
recognition accuracy, is beneficial to next target positioning, and
has a significant improvement on a network recognition speed
integrally.
Inventors: |
Zhai; Yikui (Guangdong,
CN), Deng; Wenbo (Guangdong, CN), Xu;
Ying (Guangdong, CN), Gan; Junying (Guangdong,
CN), Zeng; Junying (Guangdong, CN), Ying;
Zilu (Guangdong, CN), Ke; Qirui (Guangdong,
CN), Zhou; Wenlue (Guangdong, CN) |
Applicant: |
Name |
City |
State |
Country |
Type |
WUYI University |
Guangdong |
N/A |
CN |
|
|
Assignee: |
WUYI University (Guangdong,
CN)
|
Family
ID: |
1000005576004 |
Appl.
No.: |
16/530,766 |
Filed: |
August 2, 2019 |
Prior Publication Data
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|
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Document
Identifier |
Publication Date |
|
US 20210012146 A1 |
Jan 14, 2021 |
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Foreign Application Priority Data
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Jul 12, 2019 [CN] |
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201910630658.3 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06K
9/0063 (20130101); G06K 9/629 (20130101); G01S
7/40 (20130101); G01S 13/9027 (20190501); G06K
9/46 (20130101); G06K 9/6256 (20130101); G06K
9/3233 (20130101); G06T 3/40 (20130101); G06K
9/6262 (20130101); G06T 2207/20081 (20130101) |
Current International
Class: |
G06K
9/00 (20060101); G06K 9/62 (20060101); G06T
3/40 (20060101); G01S 13/90 (20060101); G01S
7/40 (20060101); G06K 9/32 (20060101); G06K
9/46 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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104751183 |
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Jul 2015 |
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CN |
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106156744 |
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Nov 2016 |
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CN |
|
107423734 |
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Dec 2017 |
|
CN |
|
108872988 |
|
Nov 2018 |
|
CN |
|
WO-2017160273 |
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Sep 2017 |
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WO |
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Primary Examiner: Bella; Matthew C
Assistant Examiner: Broughton; Kathleen M
Attorney, Agent or Firm: Farjami & Farjami LLP
Claims
What is claimed is:
1. A method for multi-scale synthetic aperture radar (SAR) image
recognition based on attention mechanism, comprising: inputting a
SAR training image to train and adjust an original image
recognition network, wherein the original image recognition network
comprises an attention prediction subnet, a region-of-interest
positioning subnet and an image classification subnet connected in
sequence; and inputting a SAR image to be detected to the trained
image recognition network to process and output a classification
result; wherein training and adjusting the original image
recognition network comprise: processing the SAR training image by
the attention prediction subnet to obtain an attention prediction
map, and calculating an attention prediction loss; processing the
SAR training image by the region-of-interest positioning subnet in
combination with the attention prediction map to obtain a
preliminary positioning SAR image, and calculating a
region-of-interest positioning loss; processing the preliminary
positioning SAR image by the image classification subnet to output
a classification result, and calculating a classification loss; and
calculating a network loss according to the attention prediction
loss, the region-of-interest positioning loss and the
classification loss, and adjusting the original image recognition
network according to the network loss, wherein the network loss is
Loss=.alpha.Loss.sub.a+.beta.Loss.sub.f+.gamma.Loss.sub.c, wherein
Loss.sub..alpha. is the attention prediction loss, Loss.sub.f is
the region-of-interest positioning loss and Loss.sub.c is the
classification loss, and wherein .alpha., .beta. and .gamma. are
hyper-parameters that balance among the attention prediction loss,
the region-of-interest positioning loss and the classification
loss.
2. The method for multi-scale SAR image recognition based on
attention mechanism according to claim 1, further comprising:
performing region framing and screening on the preliminary
positioning SAR image after obtaining the preliminary positioning
SAR image to obtain an optimized positioning image with a candidate
frame region feature, wherein the optimized positioning image is
used as an input of the image classification subnet in the
classification training.
3. The method for multi-scale SAR image recognition based on
attention mechanism according to claim 1, wherein processing the
SAR training image by the attention prediction subnet to obtain the
attention prediction map, and calculating the attention prediction
loss comprise: extracting RGB channel information of the SAR
training image and expressing the RGB channel information by a
tensor, and processing the SAR training image by eight building
blocks according to the tensor to obtain a multi-scale feature;
matching a weight for the SAR training image according to the
multi-scale feature to obtain a positioning feature; performing
normalization processing and deconvolution processing on the
positioning feature in combination with the SAR image to obtain the
attention prediction map; and calculating the attention prediction
loss.
4. The method for multi-scale SAR image recognition based on
attention mechanism according to claim 2, wherein processing the
SAR training image by the attention prediction subnet to obtain the
attention prediction map, and calculating the attention prediction
loss comprise: extracting RGB channel information of the SAR
training image and expressing the RGB channel information by a
tensor, and processing the SAR training image by eight building
blocks according to the tensor to obtain a multi-scale feature;
matching a weight for the SAR training image according to the
multi-scale feature to obtain a positioning feature; performing
normalization processing and deconvolution processing on the
positioning feature in combination with the SAR image to obtain the
attention prediction map; and calculating the attention prediction
loss.
5. The method for multi-scale SAR image recognition based on
attention mechanism according to claim 1, wherein obtaining the
preliminary positioning SAR image comprises: masking the SAR
training image by the attention prediction map in form of a heat
map to generate a mask and extracting a mask feature; aligning a
region-of-interest; and calculating the region-of-interest
positioning loss.
6. The method for multi-scale SAR image recognition based on
attention mechanism according to claim 2, wherein obtaining the
preliminary positioning SAR image comprises: masking the SAR
training image by the attention prediction map in form of a heat
map to generate a mask and extracting a mask feature; aligning a
region-of-interest; and calculating the region-of-interest
positioning loss.
7. An apparatus for multi-scale synthetic aperture radar (SAR)
image recognition based on, comprising: a training module
configured to input a SAR training image to train and adjust an
original image recognition network, wherein the original image
recognition network comprises an attention prediction subnet, a
region-of-interest positioning subnet and an image classification
subnet connected in sequence; and a classification module connected
with the training module and configured to input a SAR image to be
detected to the image recognition network trained by the training
module to process and output a classification result; the training
module comprising: an attention prediction module configured to
process the SAR training image by an attention prediction subnet to
obtain an attention prediction map, and calculate an attention
prediction loss; a preliminary positioning module configured to
process the SAR training image by a region-of-interest positioning
subnet in combination with the attention prediction map to obtain a
preliminary positioning SAR image, and calculate a
region-of-interest positioning loss; a classification training
module configured to process the preliminary positioning SAR image
by the image classification subnet to output the classification
result, and calculate a classification loss; and a network
adjustment module configured to calculate a network loss according
to the attention prediction loss, the region-of-interest
positioning loss and the classification loss, and adjust the
original image recognition network according to the network loss,
wherein the network loss is
Loss=.alpha.Loss.sub.a+.beta.Loss.sub.f+.gamma.Loss.sub.c, wherein
Loss.sub..alpha. is the attention prediction loss, Loss.sub.f is
the region-of-interest positioning loss and Loss.sub.c is the
classification loss, and wherein .alpha., .beta. and .gamma. are
hyper-parameters that balance among the attention prediction loss,
the region-of-interest positioning loss and the classification
loss.
8. The apparatus according to claim 7, further comprising: a
positioning optimization module connected with the classification
training module and configured to perform region framing and
screening on the preliminary positioning SAR image to obtain an
optimized positioning image with a candidate frame region feature;
wherein the optimized positioning image is used as an input of the
classification training module.
Description
CROSS REFERENCE TO RELATED APPLICATION
This application claims the benefit of CN patent application No.
201910630658.3 filed on Jul. 12, 2019, the entire disclosures of
which are hereby incorporated herein by reference.
TECHNICAL FIELD
The present disclosure relates to the field of image processing,
and more particularly, to a method and an apparatus for multi-scale
SAR image recognition based on attention mechanism.
BACKGROUND
Synthetic Aperture Radar (SAR) is widely used in military, disaster
monitoring and other fields due to its advantages of all weather
and long-distance detection, multiple angles and multiple
resolutions, thus detecting and positioning different targets.
Meanwhile, SAR image recognition is affected by inherent ambiguity
of SAR imaging, insufficient target data and other factors,
resulting in insufficient target recognition accuracy in
classification recognition. This greatly increases the difficulty
of SAR image recognition, resulting in long processing time and low
accuracy of SAR image processing.
SUMMARY
The present disclosure is intended to solve at least one of the
technical problems in the prior art, and provides a method and an
apparatus for multi-scale SAR image recognition based on attention
mechanism to effectively improve a SAR image recognition
performance by attention mechanism.
A technical solution employed by the present disclosure to solve
the technical problems thereof is as follows.
According to a first aspect, the present disclosure provides a
method for multi-scale SAR image recognition based on attention
mechanism, which comprises the following steps of:
a training step: inputting a SAR training image to train and adjust
an original image recognition network, wherein the image
recognition network comprises an attention prediction subnet, a
region-of-interest positioning subnet and an image classification
subnet connected in sequence; and
a classification step: inputting a SAR image to be detected to the
trained image recognition network to process and output a
classification result;
the training step comprising:
attention prediction: processing a SAR training image by the
attention prediction subnet to obtain an attention prediction map,
and calculating an attention prediction loss;
preliminary positioning: processing the SAR training image by the
region-of-interest positioning subnet in combination with the
attention prediction map to obtain a preliminarily positioning SAR
image, and calculating a region-of-interest positioning loss;
classification training: processing the preliminarily positioning
SAR image by the image classification subnet to output a
classification result, and calculating a classification loss;
and
network adjustment: calculating a network loss according to the
attention prediction loss, the region-of-interest positioning loss
and the classification loss, and adjusting the image recognition
network according to the network loss.
According to the first aspect of the present disclosure, the method
for multi-scale SAR image recognition based on attention mechanism
further comprises the following step of:
positioning optimization: performing region framing and screening
on the preliminarily positioning SAR image after obtaining the
preliminarily positioning SAR image to obtain an optimized
positioning image with a candidate frame region feature, wherein
the optimized positioning image is used as an input of the image
classification subnet in the classification training step.
According to the first aspect of the present disclosure, the
attention prediction step specifically comprises:
extracting RGB channel information of the SAR training image and
expressing the RGB channel information by a tensor, and processing
the SAR training image by eight building blocks according to the
tensor to obtain a multi-scale feature;
matching a weight for the SAR training image according to the
multi-scale feature to obtain a positioning feature;
performing normalization processing and deconvolution processing on
the positioning feature in combination with the SAR image to obtain
the attention prediction map; and
calculating the attention prediction loss.
According to the first aspect of the present disclosure, the
preliminary positioning step specifically comprises:
masking the SAR training image by the attention prediction map in
form of a heat map to generate a mask and extracting a mask
feature;
obtaining the preliminarily positioning SAR image by aligning a
region-of-interest; and
calculating the region-of-interest positioning loss.
According to the first aspect of the present disclosure, the
network loss is
Loss=.alpha.Loss.sub.a+.beta.Loss.sub.f+.gamma.Loss.sub.c,
Loss.sub..alpha., Loss.sub.f and Loss.sub.c are the attention
prediction loss, the region-of-interest positioning loss and the
classification loss respectively, and .alpha., .beta. and .gamma.
are hyper-parameters that balance among the attention prediction
loss, the region-of-interest positioning loss and the
classification loss.
According to a second aspect, the present disclosure provides an
apparatus applying the method for multi-scale SAR image recognition
based on attention mechanism, which comprises:
a training module configured to input a SAR training image to train
and adjust an original image recognition network, wherein the image
recognition network comprises an attention prediction subnet, a
region-of-interest positioning subnet and an image classification
subnet connected in sequence;
and a classification module connected with the training module and
configured to input a SAR image to be detected to the image
recognition network trained by the training module to process and
output a classification result;
the training module specifically comprising:
an attention prediction module configured to process the SAR
training image by an attention prediction subnet to obtain an
attention prediction map, and calculate an attention prediction
loss;
a preliminary positioning module configured to process the SAR
training image by a region-of-interest positioning subnet in
combination with the attention prediction map to obtain a
preliminarily positioning SAR image, and calculate a
region-of-interest positioning loss;
a classification training module configured to process the
preliminarily positioning SAR image by the image classification
subnet to output the classification result, and calculate a
classification loss; and
a network adjustment module configured to calculate a network loss
according to the attention prediction loss, the region-of-interest
positioning loss and the classification loss, and adjust the image
recognition network according to the network loss.
The apparatus according to the second aspect of the present
disclosure further comprises: a positioning optimization module
connected with the classification training module and configured to
perform region framing and screening on the preliminarily
positioning SAR image to obtain an optimized positioning image with
a candidate frame region feature; wherein the optimized positioning
image is used as an input of the classification training
module.
The technical solutions provided by the present disclosure at least
have the following beneficial effects: the SAR image is processed
by the attention prediction subnet to generate the attention
prediction map, and the most significant feature part in the SAR
image is positioned by the attention prediction subnet, which
greatly eliminates a redundancy of image features in a machine
vision, the region-of-interest of the target is effectively
determined by the attention prediction subnet, which reduces
interference of image noises, greatly reduces an image processing
time, improves a target recognition accuracy, is beneficial to next
target positioning, and has a significant improvement on a network
recognition speed integrally.
BRIEF DESCRIPTION OF THE DRAWINGS
The present disclosure is further described below with reference to
the drawings and the embodiments.
FIG. 1 is a flow chart of a method for multi-scale SAR image
recognition based on attention mechanism according to an embodiment
of the present disclosure;
FIG. 2 is a flow chart of a method for multi-scale SAR image
recognition based on attention mechanism according to another
embodiment of the present disclosure;
FIG. 3 is a structure schematic diagram of an apparatus applying
the method for multi-scale SAR image recognition based on attention
mechanism according to an embodiment of the present disclosure;
and
FIG. 4 is a structure schematic diagram of an apparatus applying
the method for multi-scale SAR image recognition based on attention
mechanism according to another embodiment of the present
disclosure.
DETAILED DESCRIPTION
The example embodiments of the disclosure are described in detail.
The preferred embodiments of the disclosure are shown in the
drawings, and the purpose of the drawings is to supplement the
description in the written part of the description with graphics,
so that people can intuitively and vividly understand each
technical feature and an overall technical solution of the
disclosure, but it cannot be understood as limiting the protection
scope of the disclosure.
In the description of the disclosure, unless otherwise clearly
defined, words such as setting, installation, connection, etc.,
should be understood broadly, and those skilled in the art can
reasonably determine the specific meanings of the above words in
the disclosure with reference to the specific contents of the
technical solution.
Referring to FIG. 1, an embodiment of the present disclosure
provides a method for multi-scale SAR image recognition based on
attention mechanism, which comprises the following steps of:
step S100: a training step: inputting a SAR training image to train
and adjust an original image recognition network 10, wherein the
image recognition network 10 comprises an attention prediction
subnet 11, a region-of-interest positioning subnet 12 and an image
classification subnet 13 connected in sequence; and
step S200: a classification step: inputting a SAR image to be
detected to the trained image recognition network 10 to process and
output a classification result;
the step S100 comprising:
the step S110: attention prediction: processing a SAR training
image by the attention prediction subnet 11 to obtain an attention
prediction map, and calculating an attention prediction loss;
the step S120: preliminary positioning: processing the SAR training
image by the region-of-interest positioning subnet 12 in
combination with the attention prediction map to obtain a
preliminarily positioning SAR image, and calculating a
region-of-interest positioning loss;
the step S130: classification training: processing the
preliminarily positioning SAR image by the image classification
subnet 13 to output a classification result, and calculating a
classification loss; and
the step S140: network adjustment: calculating a network loss
according to the attention prediction loss, the region-of-interest
positioning loss and the classification loss, and adjusting the
image recognition network 10 according to the network loss.
In the embodiment, a large number of SAR training images are input
to train and adjust the original image recognition network 10 to
improve a recognition degree of the image recognition network 10;
and then the SAR image to be detected is recognized and classified.
The SAR image is processed by the attention prediction subnet 11 to
generate the attention prediction map, and the most significant
feature part in the SAR image is positioned by the attention
prediction subnet 11, which greatly eliminates a redundancy of
image features in a machine vision, the region-of-interest of the
target is effectively determined by the attention prediction subnet
11, which reduces interference of image noises, greatly reduces an
image processing time, improves a target recognition accuracy, and
is beneficial to next target positioning.
Referring to FIG. 2, a method for multi-scale SAR image recognition
based on attention mechanism according to another embodiment
further comprises the following steps of:
step S150: positioning optimization: performing region framing and
screening on the preliminarily positioning SAR image after
obtaining the preliminarily positioning SAR image to obtain an
optimized positioning image with a candidate frame region feature;
more specifically, passing the preliminarily positioning SAR image
through a region candidate frame network to generate a detection
frame region; comparing an Intersection over Union of the detection
frame region and a true value region with a threshold, and
outputting a positive sample image in which the Intersection over
Union of the detection frame region and the true value region is
greater than the threshold; and screening k optimized positioning
images with a candidate frame region feature and a maximum
confidence value by using a non-maximum suppression method. In the
next classification training step, the optimized positioning image
is used as an input of the image classification subnet 13. The
preliminarily positioning SAR image is further screened and
optimized to improve the classification accuracy.
Further, the step S110 specifically comprises the following
steps.
In step S111, RGB channel information of the SAR training image is
extracted and expressed by a tensor, and the SAR training image is
processed by eight building blocks according to the tensor to
obtain four multi-scale features, with sizes of 64.times.64,
32.times.32, 16.times.16 and 8.times.8 respectively. Specifically,
the tensor has a size of 128.times.128.times.3.
In step S112, a weight is matched for the SAR training image
according to the multi-scale feature to obtain a positioning
feature; in order to selectively screen a small amount of important
information from a large amount of image information, ignore most
unimportant information, and pay attention on these important
information, different attention weights are assigned to the image
with the multi-scale feature output by each building block, and
attention is paid on the concerned part in the SAR images, wherein
the focusing process is embodied in the calculation of a weight
coefficient. When the weight is larger, more attentions are paid on
the information, i.e. the weight represents the importance of the
information. The positioning feature is calculated according to the
following formula:
.times..function. ##EQU00001## wherein a first process is to
calculate the weight coefficient according to a parameter Query and
a multi-scale feature Key.sub.i, while a second process is to
perform weighted sum on an image region Value.sub.i according to
the weight coefficient. The first process can be further subdivided
into two stages: a similarity or a correlation between the
parameter Query and the multi-scale feature Key.sub.i is calculated
according to the parameter Query and the multi-scale feature
Key.sub.i in the first stage; and an original score in the first
stage is normalized in the second stage.
In step S113, normalization processing and deconvolution processing
are performed on the positioning feature in combination with the
SAR image to obtain the attention prediction map.
In step S114, the attention prediction loss is calculated. The
attention prediction loss is
.times..times..times..times..function. ##EQU00002## wherein
A.sub.ij refers to each element in the attention prediction map,
A.sub.ij refers to the attention prediction map, i and j refers to
a length and a width of the attention prediction map, and I and J
refer to sets of i and j respectively.
Further, the step S120 specifically comprises:
step S121: masking the SAR training image by the attention
prediction map {circumflex over (V)} in form of a heat map to
generate a mask and extracting a mask feature F', with a masking
process of F'=F.circle-w/dot.{(1-.theta.){circumflex over
(V)}.sym..theta.}, wherein .theta. is a threshold for controlling
the mask, and F is a positioning feature;
step S122: obtaining the preliminarily positioning SAR image by
aligning a region-of-interest, which can effectively suppress a
redundant feature unrelated to SAR image classification and
detection, and highlight the region-of-interest; and
step S123: calculating the region-of-interest positioning loss,
wherein the region-of-interest positioning loss is
.times..times..function..times..function. ##EQU00003## and 1 is a
prediction tag of the attention prediction map.
Further, in the step S130, the image classification subnet 13 is
composed of a 7.times.7 convolution layer, a maximum pool layer,
four multi-scale modules and two fully connected layers. Four
convolution layer channels C1, C2, C3 and C4 with different core
sizes are connected by the four multi-scale modules to extract the
multi-scale feature, wherein C1 and C3 have a size of 3.times.3, C2
has a size of 5.times.5, and C4 has a size of 7.times.7; and
finally, the two fully connected layers are applied to output the
classification result. In addition, the classification loss is
.times..times..function..times..function. ##EQU00004## and a
calculation mechanism is the same as the region-of-interest
positioning loss.
Further, in the step S140, the network loss is
Loss=.alpha.Loss.sub.a+.beta.Loss.sub.f+.gamma.Loss.sub.c, wherein
Loss.sub..alpha., Loss.sub.f and Loss.sub.c are the attention
prediction loss, the region-of-interest positioning loss and the
classification loss respectively, and .alpha., .beta. and .gamma.
are hyper-parameters that balance among the attention prediction
loss, the region-of-interest positioning loss and the
classification loss. It should be noted that in an early stage of
training, .alpha.>>.beta.=.gamma. is set to accelerate a
convergence speed of the attention prediction subnet 11; and in
middle and later stages of training, .alpha.<<.beta.=.gamma.
is set to minimize the region-of-interest positioning loss and the
classification loss, and improve a convergence of attention
prediction.
Another embodiment of the present disclosure provides an apparatus
applying the method for multi-scale SAR image recognition based on
attention mechanism, which comprises:
a training module 1 configured to input a SAR training image to
train and adjust an original image recognition network 10, wherein
the image recognition network 10 comprises an attention prediction
subnet 11, a region-of-interest positioning subnet 12 and an image
classification subnet 13 connected in sequence;
and a classification module 2 connected with the training module 1
and configured to input a SAR image to be detected to the image
recognition network 10 trained by the training module 1 to process
and output a classification result;
the training module 1 specifically comprising:
an attention prediction module 3 configured to process the SAR
training image by an attention prediction subnet 11 to obtain an
attention prediction map, and calculate an attention prediction
loss;
a preliminary positioning module 4 configured to process the SAR
training image by a region-of-interest positioning subnet 12 in
combination with the attention prediction map to obtain a
preliminarily positioning SAR image, and calculate a
region-of-interest positioning loss;
a classification training module 5 configured to process the
preliminarily positioning SAR image by the image classification
subnet 13 to output the classification result, and calculate a
classification loss; and
a network adjustment module 6 configured to calculate a network
loss according to the attention prediction loss, the
region-of-interest positioning loss and the classification loss,
and adjust the image recognition network 10 according to the
network loss.
The apparatus according to another embodiment further comprises: a
positioning optimization module 7 connected with the classification
training module 5 and configured to perform region framing and
screening on the preliminarily positioning SAR image to obtain an
optimized positioning image with a candidate frame region feature;
wherein the optimized positioning image is used as an input of the
classification training module 5.
Another embodiment of the present disclosure further provides an
apparatus, which comprises a processor and a memory for connecting
to the processor, wherein the memory stores an instruction
executable by the processor, and the instruction is executed by the
processor to enable the processor to execute the method for
multi-scale SAR image recognition based on attention mechanism
above.
Another embodiment of the present disclosure provides a storage
medium storing a computer-executable instruction, wherein the
computer-executable instruction is configured to make a computer
execute the method for multi-scale SAR image recognition based on
attention mechanism above.
The foregoing is only preferred embodiments of the disclosure, but
the present disclosure is not limited to the embodiments above. Any
technical effect of the disclosure implemented by using the same
means shall fall within the protection scope of the disclosure.
* * * * *